Owing to recent advances in digital technology, it is now possible to convert a radiation image or the like to a digital image signal and subject the digital image signal to image processing such as frequency processing to output the processed image by displaying it on a display such as a CRT or printing it on film by a printer. In such frequency processing, image processing is executed by decomposing the image of interest into image coefficients of a plurality of frequency bands and increasing or decreasing the image coefficient values of every frequency band individually.
On the other hand, it is common practice to uniformalize a histogram (frequency distribution) of image densities (pixel values) by using a density histogram uniformalizing method [e.g., see “Digital Image Processing [1] for Understanding Images”, pp. 133-135, by Junichiro Toriwaki, published by Shokodo, First Edition, Fourth Printing). Using this method makes it possible to utilize a given density range efficiently so that image contrast is improved.
The above method attempts to obtain a desired frequency processing effect by changing the values of frequency coefficients. However, this means nothing more than altering the strength of coefficient values and does not involve the idea of controlling a histogram of coefficient values after a coefficient conversion. More specifically, the conventional method of increasing or decreasing coefficient values involves only increasing or decreasing coefficient values individually on a per-frequency-band basis and does not take into consideration a coefficient histogram of each frequency band. This makes it necessary to adjust coefficients by trial and error in order to obtain the desired effect. Further, when the value of a frequency coefficient is merely changed, this has an effect upon the dynamic range of the image after it has been processed and there are instances where artifacts such as overshoot occur at the edge (contour) of the image.
Further, though image contrast can be improved by uniformalizing a pixel-value histogram, as indicated in the reference cited above, this method implements gray-level conversion processing and not frequency processing, thereby making it impossible to adjust components on a per-frequency-band basis.
The present inventors have discovered that an image of desired contrast is obtained by controlling the frequency distribution of coefficient values of every frequency band in accordance with the particular objective. In the examples of the prior art mentioned above, however, there is no implementation of a coefficient conversion that takes into account a frequency distribution of coefficient values obtained after a coefficient conversion.
In view of the state of the prior art described above, there is need for an image processing apparatus and method whereby the contrast of an image of interest can be improved efficiently and effectively.